James L. Weaver
Developer Advocate
 


jweaver@pivotal.io
JavaFXpert.com

twitter.com/JavaFXpert

Machine Learning

The Fundamentals

SpringOne Tour / Istanbul

About Presenter James Weaver

Java Champion, JavaOne Rockstar, plays well with others, etc :-)

Author of several Java/JavaFX/RaspPi books

Developer Advocate & International Speaker for

Mission: "Transform how the world builds software"

Mission: "Transform how the world builds software"

Some Pivotal involvement in machine learning

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From introductory video in Machine Learning course (Stanford University & Coursera) taught by Andrew Ng.

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Self-driving cars

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Generating image descriptions

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Supervised Learning

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S​upervised learning regression problem

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Unsupervised Learning

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Unsupervised learning finds structure in unlabeled data

(e.g. market segment discovery, and social network analysis)

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Reinforcement Learning

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AlphaGo is a recent reinforcement learning success story

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Supervised Learning

(Let's dive in now)

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Supervised learning classification problem

(using the Iris flower data set)

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Visualizing Iris dataset with TensorFlow tool

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Modeling the brain works well with machine learning
(ya think?)

(inputs)

(output)

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Anatomy of an Artificial Neural Network

(aka Deep Belief Network when multiple hidden layers)

 

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Neural net visualization app (uses Spring and DL4J)

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Entering feature values for prediction (classification)

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Simple neural network trained for XOR logic

forward propagation

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Feedforward calculations with XOR example

For each layer:

Multiply inputs by weights:

(1 x 8.54) + (0 x 8.55) = 8.54

Add bias:

8.54 + (-3.99) = 4.55

Use sigmoid activation function:

1 / (1 + e

-4.55

) = 0.99

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Excellent video on neural networks

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Simple neural network trained for XOR logic

back propagation (minimize cost function)

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Lab Exercise / Visualizing Gradient Descent:

Linear Regression app developed by Katharine Beaumont

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Make Your Own Neural Network (book)

Contains details on how weights and biases are adjusted during back propagation

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Excellent video on gradient descent

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Visual Neural Network application architecture

Spring makes REST services and WebSockets easy as π

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The app leverages machine learning libraries found at deeplearning4j.org

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Code that configures our speed dating neural net

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To quickly create a Spring project, visit start.spring.io

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Lab Exercise:

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For each of the four dataset icons (Circle, Exclusive Or, Gaussian, and Spiral):

  1. Select only the X1 & X2 features
  2. Modify the hyperparameters in such a way that minimizes the number of Epochs required to make the Test loss and Training loss each <= 0.009
  3. Tweet screenshot with your lowest Epochs result tagging @JavaFXpert in the message.

Practice tuning neural network hyperparameters

Is Optimizing your Neural Network a Dark Art ?

Excellent article by Preetham V V on neural networks and choosing hyperparameters

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Various Neural Networks

Convolutional Neural Network for recognizing images

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Convolutional neural network architecture

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Peeking into a convolutional neural network

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Time series prediction with neural networks

What is happening?  What is most likely to happen next?

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This is a job for a Recurrent Neural Network

What is happening?  What is most likely to happen next?

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Recurrent Neural Network

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vs. traditional feed-forward network

Music composition with an RNN

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Predicting the most likely next note

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Playing a duet with neural networks

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Playing a duet with neural networks

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Unsupervised Learning

@KatharineCodes  @JavaFXpert

(Let's dive in now)

Using unsupervised learning to map artworks

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Euclidian distance for high-dimensional vectors

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Using unsupervised learning to map words

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word2vec vector representations of words

Using unsupervised learning to map words

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word2vec vector offsets for gender relationships

Using unsupervised learning to map words

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word2vec vector offsets for plural relationships

Using unsupervised learning to map words

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word2vec vector arithmetic

word2vec vector arithmetic

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King – Man + Woman = Queen

Visualizing word2vec words & points

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using Tensorflow Embedding Projector

Inspecting word embeddings

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Reinforcement Learning

(Let's dive in now)

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Reinforcement Learning tabula rasa

Using BURLAP for Reinforcement Learning

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Learning to Navigate a Grid World with Q-Learning

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Rules of this Grid World

  • Agent may move left, right, up, or down (actions)
  • Reward is 0 for each move
  • Reward is 5 for reaching top right corner (terminal state)
  • Agent can't move into a wall or off-grid
  • Agent doesn't have a model of the grid world.  It must discover as it interacts.

Challenge: Given that there is only one state that gives a reward, how can the agent work out what actions will get it to the reward?

(AKA the credit assignment problem)

Goal of an episode is to maximize total reward

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This Grid World's MDP (Markov Decision Process)

In this example, all actions are deterministic

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Agent learns optimal policy from interactions with the environment (s, a, r, s')

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Visualizing training episodes

From BasicBehavior example in  https://github.com/jmacglashan/burlap_examples

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Expected future discounted rewards, and polices

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This example used discount factor 0.9

Low discount factors cause agent to prefer immediate rewards

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How often should the agent try new paths vs. greedily taking known paths?

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Q-Learning approach to reinforcement learning

Left Right Up Down
...
2, 7 2.65 4.05 0.00 3.20
2, 8 3.65 4.50 4.50 3.65
2, 9 4.05 5.00 5.00 4.05
2, 10 4.50 4.50 5.00 3.65
...

Q-Learning table of expected values (cumulative discounted rewards) as a result of taking an action from a state and following an optimal policy.  Here's an explanation of how calculations in a Q-Learning table are performed.

Actions

States

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Tic-Tac-Toe with Reinforcement Learning

Learning to win from experience rather than by being trained

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Inspired by the Tic-Tac-Toe Example section...

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Tic-Tac-Toe Learning Agent and Environment

X

O

Our learning agent is the "X" player, receiving +5 for winning, -5 for losing, and -1 for each turn

The "O" player is part of the Environment.  State and reward updates that it gives the Agent consider the "O" play.

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Tic-Tac-Toe state is the game board and status

States 0 1 2 3 4 5 6 7 8
O I X I O X X I O, O won N/A N/A N/A N/A N/A N/A N/A N/A N/A
I  I  I  I  I  I O I X, in prog 1.24 1.54 2.13 3.14 2.23 3.32 N/A 1.45 N/A
I  I O I  I X O I X, in prog 2.34 1.23 N/A 0.12 2.45 N/A N/A 2.64 N/A
I  I O O X X O I X, in prog +4.0 -6.0 N/A N/A N/A N/A N/A -6.0 N/A
X I O I  I X O I X, X won N/A N/A N/A N/A N/A N/A N/A N/A N/A
...

Q-Learning table of expected values (cumulative discounted rewards) as a result of taking an action from a state and following an optimal policy

Actions (Possible cells to play)

Unoccupied cell represented with an I in the States column

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Tic-Tac-Toe with Reinforcement Learning

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Through the Eyes of a Self-Driving Tesla

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Summary of links

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James L. Weaver
Developer Advocate
 


jweaver@pivotal.io
JavaFXpert.com

@JavaFXpert

Machine Learning

The Fundamentals

Machine Learning Exposed: The Fundamentals

By javafxpert

Machine Learning Exposed: The Fundamentals

Machine Learning Exposed fundamental concepts. Shedding light on machine learning.

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